Abstract

Introduction

   Prostate cancer (PC) is one of the most frequently diagnosed cancers in
   males. MiR-153, as a member of the microRNA (miRNA) family, plays an
   important role in PC. This study aims to explore the expression and
   possible molecular mechanisms of the miR-153 action.

Methods

   Formalin-fixed paraffin-embedded (FFPE) tissues were collected from
   prostatectomy specimens of 29 metastatic and 32 initial stage PC
   patients. Expression levels of miR-153 were measured using real-time
   reverse transcription polymerase chain reaction (qRT-PCR). 2−ΔΔCT
   method was used for quantitative gene expression assessment. The
   candidate target genes for miR-153 were predicted by TargetScan.
   Mutations in target genes of miR-153 were identified using exome
   sequencing. Protein-protein interaction (PPI) networks, Kyoto
   Encyclopedia of Genes and Genomes (KEGG) analyses were performed to
   investigate the potential molecular mechanisms of miR-153 in PC.

Results

   MiR-153 was significantly up-regulated in PC tissues compared to
   non-cancerous tissues. The analysis of correlation between the
   expression level of miR-153 and clinicopathological factors revealed a
   statistically significant correlation with the stage of the tumor
   process according to tumor, node, metastasis (TNM) staging system
   (p = 0.0256). ROC curve analysis was used to evaluate the predictive
   ability of miR-153 for metastasis development and it revealed miR-153
   as a potential prognostic marker (AUC = 0.85; 95%CI 0.75–0.95;
   sensitivity = 0.72, specificity = 0.86)). According to logistic
   regression model the high expression of miR-153 increased the risk of
   metastasis development (odds ratios = 3.14, 95% CI 1.62–8.49;
   p-value = 0.006). Whole exome sequencing revealed nonsynonymous somatic
   mutations in collagen type IV alpha 1 (COL4A1), collagen type IV alpha
   3 (COL4A3), forkhead box protein O1 (FOXO1), 2-hydroxyacyl-CoA lyase 1
   (HACL1), hypoxia-inducible factor 1-alpha (HIF-1A), and nidogen 2
   (NID2) genes. Moreover, KEGG analysis revealed that the extracellular
   matrix–receptor (ECM-receptor) interaction pathway is mainly involved
   in PC.

Conclusion

   MiR-153 is up-regulated in PC tissues and may play an important role in
   aggressive PC by targeting potential target genes.

   Keywords: Gene expression, Aggressive prostate cancer, miR-153, Target
   gene mutations, Oncogenesis, Biomarker

1. Introduction

   Prostate cancer (PC) is a commonly diagnosed condition, with an
   estimated 1414.3 thousand new cases of PC and 375.3 thousand deaths
   from the disease in 2020 [[39]1]. In one third of PC patients the tumor
   progresses (perineural and stromal invasion) after initial regression
   in response to androgen deprivation therapy [[40]2]. Despite current
   advances in surgical, chemotherapeutic and radiological methods of
   treatment, the five-year survival rate in castration-resistant patients
   is approximately 31.0% [[41]3]. Most PC-related deaths occur due to the
   inability of existing treatments to prevent tumor spread [[42]4].

   Rectal examination and prostate specific antigen (PSA) levels are used
   to diagnose PC. However, the medical and scientific communities have
   questioned routine PSA testing. PSA has high sensitivity but very low
   specificity for PC. It can be elevated in the presence of benign
   prostate disease, infection, inflammation or benign hyperplasia
   [[43]5]. Routine PSA testing leads to a high percentage of
   false-positive results. Moreover, PSA levels correlate poorly with the
   stage of the disease, leading to misdiagnoses and overtreatment of
   indolent forms of PC [[44]6]. Considering all the above, there is a
   need for new molecular genetic markers capable of both detecting the
   disease at the earliest stage and predicting its course.

   MicroRNAs (miRNAs) are considered to be one of the most promising
   markers for various diseases including PC. The discovery of miRNAs
   provided a conceptual breakthrough in cancer research. MiRNAs are
   non-coding RNAs (ncRNAs) (19–22 nucleotides) which are involved in
   post-transcriptional regulation of gene expression. MiRNAs are
   increasingly associated with the initiation, development and
   progression of malignancies. Aberrant miRNAs expression has been
   identified in a variety of malignant tumors, with recent evidence
   suggesting that miRNAs function as tumor suppressor genes and/or
   oncogenes [[45]7]. Recent studies have various miRNAs expression
   profiles in PC tissues [[46][8], [47][9], [48][10], [49][11]]. MiR-153
   was found to be downregulated in various cancers, such as breast cancer
   (BC), gastric cancer (GC) and oral cancer (OC) [[50]15]. However, there
   are few investigations devoted to miR-153 and its role PC [[51][12],
   [52][13], [53][14], [54][15]].

   In this study, we evaluate the expression levels of miR-153 in PC
   tissue specimens and adjacent normal tissue specimens, the association
   of miR-153 with clinical characteristics and mutations in target genes
   of miR-153 and identified the pathways involved in PC progression. Our
   study was aimed to evaluate and understand the expression level of
   miR-153 in malignant tumor and normal prostate tissue in patients with
   metastatic PC and initial stages of the disease and possible molecular
   mechanism of the miR-153.

2. Material and methods

2.1. Patients and samples collection

   In this study, 61 PC patients (average age 60, range 41–80 years) who
   underwent surgical treatment between 2008 and 2020 in Bashkir State
   Medical University hospital were included. Ethical approval for this
   study was obtained from Institute of Biochemistry and Genetics
   Bioethics Committee. All samples investigated in this study were
   obtained with written informed consents of the participants. We
   collected formalin-fixed paraffin-embedded (FFPE) tissues from
   prostatectomy material including 29 metastatic and 32 localized (stages
   I, II) PC patients. The samples were classified using the tumor, node,
   metastasis (TNM) staging system from clinical stages I-IV.

2.2. Samples evaluation

   After tissue section were obtained, Hematoxylin and Eosin (H&E)
   staining was performed and the slides were examined by two independent
   experienced pathologists. DNA and RNA were isolated from tumor regions
   and normal prostate tissue from each patient. The healthy region of the
   prostate without tumor cells in the selected FFPE block, was taken as a
   source of normal RNA and DNA. Total RNA and DNA extraction was
   performed using Quick-DNA/RNA™ FFPE Kits (Zymo Research) following the
   manufacturer's protocol. The process consisted of simple tissue
   deparaffinization in deparaffinization solution, proteinase K treatment
   followed by RNA and DNA isolation in spin columns.

2.3. Real-time reverse transcription polymerase chain reaction (qRT-PCR)

   The PCR amplification for the quantification of the miR-153 and U6 RNA
   was performed using a TaqMan MicroRNA Reverse Transcription Kit
   (Applied Biosystems; Life Technologies Corp) and TaqMan Human MicroRNA
   Assay Kit (Applied Biosystems; Life Technologies Corp). Expression
   levels were measured using CFX96™ PCR detection system (BioRad). All
   reactions were performed three times for each sample. The 2−ΔΔCT method
   was used for quantitative gene expression assessment. The 2−ΔΔCT method
   is based on the assumption that the cycle threshold difference (ΔCt)
   between target gene and reference gene is proportional to relative
   target gene expression. The relative expression of miR-153 was shown as
   fold difference relative to U6 RNA.

2.4. Whole exome sequencing

   Patients with metastatic adenocarcinoma were chosen for exome
   sequencing to identify mutations in target genes of miR-153. Target
   genes were selected using TargetScan. DNA fragmentation, library
   preparation, and exome capture were conducted according to the
   manufacturer's recommendations. Selection of specific DNA fragments was
   conducted using the SureSelect system followed by concurrent sequencing
   of the obtained libraries using Illumina HiSeq 2000. All the reads were
   aligned with the reference genome using Burrows-Wheeler Alignment (BWA)
   software. We used the human genome sequence (Genome Reference
   Consortium Human Build 37 (GRCh37-hg19)) as a reference. Identification
   of the variants was conducted using the Genome Analysis Tool Kit
   (GATK). The identified variants were annotated by ANNOVAR software
   using the scripts table_annovar.pl and annotate_variation.pl, which
   allows to compare single nucleotide substitutions with the number of
   specialized databases and to annotate prognostic functional
   significance of the revealed alterations using six in silico software
   programs (SIFT, PolyPhen-2, LRT, Mutation Assessor, MutationTaster,
   phyloP, and GERP++) from dbNSFP v.3.0а. Additionally, we used CLINVAR
   and CADD (Combined Annotation Dependent Depletion) softwares. Exome
   sequencing procedure and bioinformatics were performed as previously
   described by Gilyazova et al. [[55]16].

2.5. Network analysis

   The network of mutated genes was generated using STRING-DB 9.1
   [[56]17]. Minimum required interaction scores were set to “high
   confidence” (0.700). The Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway enrichment analysis was performed using online KEGG tools
   ([57]http://www.kegg.jp/).

2.6. Statistical analysis

   The obtained data was analyzed using R-studio program by calculating
   average values, standard deviation, and arithmetic mean error. The data
   are presented as means ± standard deviation. To assess the significance
   of differences, the Mann–Whitney U test was used. The predictive
   ability of miR‐153 was determined by the receiver operating
   characteristic (ROC) curves. A logistic regression prediction model was
   applied to calculate prediction scores for individual samples. Changes
   were considered reliably significant at p ≤ 0.05.

3. Results

3.1. MiR-153 expression in PC tissue

   The expression level of miR-153 was analyzed in 61 samples of tumor
   tissue ([58]Fig. 1) and 61 adjacent normal prostate tissue ([59]Fig. 2)
   using qRT-PCR. MiR-153 expression showed to be increased in tumor
   tissue (mean ± SEM:1.36 ± 0.23) over normal prostatic tissue
   (mean ± SEM:0.66 ± 0.09) with p-value = 0.03 ([60]Fig. 3).

Fig. 1.

   [61]Fig. 1
   [62]Open in a new tab

   Hematoxylin and Eosin (H&E) stain of prostate cancer (PC) tissue.

Fig. 2.

   [63]Fig. 2
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   Hematoxylin and Eosin (H&E) stain of normal prostate tissue.

Fig. 3.

   [65]Fig. 3
   [66]Open in a new tab

   MiR-153 expression level in prostate cancer (PC) tissues vs adjacent
   normal prostate tissues. A statistically significant increase in
   miR-153 expression in PC tissue compared to normal tissue was
   demonstrated. The significance level (p-value) is determined using the
   Wilcoxon T-test.

3.2. MiR-153 expression and clinicopathological factors

   Patients were divided into subgroups with high (n = 31) expression and
   low (n = 30) expression of miR-153. The median miR-153 expression value
   defined the threshold value. The analysis of correlation between
   miR-153 expression and clinicopathological factors ([67]Table 1)
   revealed a significant correlation with the stage of the tumor process
   according to TNM classification (p = 0.001). Expression levels of
   miR-153 in different TNM stages of PC is presented ([68]Fig. 4). It was
   shown that miR-153 expression significantly higher (mean ± SEM:
   2.29 ± 0.41) in metastatic PC tumors compared to non-metastatic tumors
   (mean ± SEM: 0.53 ± 0.12) with p < 0.0001.

Table 1.

   Association between miR-153 expression and clinical and pathological
   characteristics of patients with prostate cancer.
     Variables    Cases (n) MiR-153 expression
     __________________________________________________________________

                             P-value
   High (n = 31) Low (n = 30)
   Age, years
   <55           15          9      6          0.818
   ≥55           46          24     22
   Lymph node metastasis
   Yes           29          22     7          0.180
   No            32          18     14
   Bone metastases
   Yes           27          18     9          0.203
   No            34          16     18
   Gleason score
   <8            23          12     11         0.710
   ≥8            38          23     15
   TNM stage
   I + II        32          8      24         0.001
   IV            29          23     6
   [69]Open in a new tab

   TNM, tumor, node, metastasis.

Fig. 4.

   [70]Fig. 4
   [71]Open in a new tab

   Expression levels of miR-153 in different tumor, node, metastasis (TNM)
   staging system of prostate cancer (PC). It was demonstrated that
   miR-153 is upregulated in advanced PC compared to localized disease.
   Significance level (p-value) was determined using the Mann-Whitney U
   test.

3.3. ROC curve of miR-153

   In addition, the receiver-operating characteristic (ROC) curve was
   drawn to further calculate the area under the curve (AUC) and
   authenticate the diagnostic ability of miR-153. Results revealed that
   miR-153 expression presented low diagnostic ability for tumor and
   normal prostate tissue discrimination. As shown at ROC curve of the
   miR-153 presented in [72]Fig. 5, the AUC was 0.61 (95% CI 0.52–0.71;
   sensitivity = 0.66, specificity = 0.53). Furthermore, logistic
   regression analysis revealed that the miR-153 was independent predictor
   for PC (odds ratios = 1.57, 95% CI 1.13–2.36; p = 0.014).

Fig. 5.

   [73]Fig. 5
   [74]Open in a new tab

   Receiver operating characteristic (ROC) curve analysis of miR-153 to
   differentiate between prostate cancer (PC) and normal prostate tissue.

   Further the ROC curve analysis was used to evaluate the predictive
   ability of miR-153 for metastasis development. As shown at [75]Fig. 6
   the AUC was 0.85 (95%CI 0.75–0.95; sensitivity = 0.72,
   specificity = 0.86) that let suggest miR-153 as potential prognostic
   marker. According to logistic regression model the high expression of
   miR-153 increased the risk of metastasis development (odds
   ratios = 3.14, 95% CI 1.62–8.49; p-value = 0.006).

Fig. 6.

   [76]Fig. 6
   [77]Open in a new tab

   Receiver operating characteristic (ROC) curve analysis of miR-153 to
   differentiate between metastatic prostate cancer (PC) and
   non-metastatic PC.

3.4. Identification of mutations in miR-153 target genes

   In order to identify mutations in miR-153 target genes we performed
   exome sequencing in all patients with metastatic PC. We identified
   nonsynonymous somatic mutations in genes collagen type IV alpha 1
   (COL4A1), collagen type IV alpha 3 (COL4A3), forkhead box protein O1
   (FOXO1), 2-hydroxyacyl-CoA lyase 1 (HACL1), hypoxia-inducible factor
   1-alpha (HIF-1A), and nidogen 2 (NID2). No pathogenic mutations were
   revealed in the coding regions of the phosphatase and tensin homolog
   deleted on chromosome 10 (PTEN) gene. None of the pathogenic mutations
   in miR-153 target genes was seen in all patients. This may be due to
   the fact that shared somatic mutations were not the cause of metastatic
   PC. The most deleterious germline and somatic mutations are summarized
   in [78]Table 2.

Table 2.

   Pathogenic mutations identified in target genes for miR-153 using exome
   sequencing.
   ID Gene Chr Pos1 Pos2 Ref Alt Hom/Het SIFT_Score SIFTpred MutAssessor
   MutTast PolyPhen2 Phylop
   ABI2 chr2 204231743 204231743 A T het 0.005 DELETERIOUS 2.27,0.678,M
   −0.525
   ACTB chr7 5567477 5567477 A G het 0.001 DELETERIOUS 4.95,0.911,H 2.114
   AQR chr15 35166090 35166090 A G het 0.002 DELETERIOUS 3.54,0.789,H
   2.115
   AQR chr15 35189132 35189132 G T het 0.000 DELETERIOUS 3.94,0.823,H
   2.743
   COL4A1* chr13 110838875 110838875 C T het 0.030 DELETERIOUS
   2.095,0.663,M 1,1.0,D 0.999,D 2.284
   rs115324397 COL4A3* chr2 228110691 228110691 C A het 0.010 DELETERIOUS
   2.61,0.708,M 1.000,1.000,D 1.0,D 2.427
   COPS3 chr17 17171261 17171261 C A het 0.013 DELETERIOUS 2.805,0.725,M
   2.572
   CSTF1 chr20 54970669 54970669 C A het 0.000 DELETERIOUS 2.12,0.665,M
   2.618
   EIF3A chr10 120802249 120802249 C A het 0.008 DELETERIOUS 2.175,0.670,M
   2.937
   rs114015346 EPRS* chr1 220193423 220193423 T C het 0.020 DELETERIOUS
   3.04,0.745,M 1,1.0,D 0.998,D 1.009
   FOXO1* chr13 41134822 41134822 C T het 0.006 DELETERIOUS 2.8,0.724,M
   1,1.0,D 1.0,D 2.763
   GSPT1 chr16 11980708 11980708 C G het 0.001 DELETERIOUS 2.295,0.681,M
   2.584
   GTPBP4 chr10 1038459 1038459 C A het 0.002 DELETERIOUS 3.535,0.788,H
   2.796
   rs74637339 HACL1* chr3 15628040 15628040 T A het 0.004 DELETERIOUS
   2.085,0.662,M 1,1.0,D 0.998,D 2.225
   rs61755705 HIF1A* chr14 62187212 62187212 G C het 0.008 DELETERIOUS
   2.55,0.703,M 1.000,1.000,D 0.978,D 1.423
   ITGA1 chr5 52221259 52221259 C T het 0.000 DELETERIOUS 2.095,0.663,M
   2.656
   ITGAV chr2 187505680 187505680 C A het 0.003 DELETERIOUS 3.625,0.796,H
   2.712
   LONP2 chr16 48311377 48311377 C A het 0.004 DELETERIOUS 2.64,0.711,M
   2.756
   rs140970953 MCM4* chr8 48883234 48883234 C T het 0.002 DELETERIOUS
   4.54,0.875,H 1,1.0,D 1.0,D 2.906
   rs61734508 NID2* chr14 52534758 52534758 C G het 0.002 DELETERIOUS
   2.325,0.683,M 0.821,0.821,D 1.0,D 1.327
   ORC1 chr1 52847394 52847394 G T het 0.000 DELETERIOUS 3.795,0.811,H
   2.661
   PBRM1 chr3 52702546 52702546 G T het 0.012 DELETERIOUS 3.325,0.770,M
   1.449
   SMAD3 chr15 67477139 67477139 C A het 0.000 DELETERIOUS 3.065,0.747,M
   2.469
   YTHDC2 chr5 112891819 112891819 A T het 0.000 DELETERIOUS 4.2,0.846,H
   0.698
   [79]Open in a new tab

   Note: SIFT: D - disease causing; MutationAssessor: H - high;
   M − medium; L – low pathogenicity; MutationTaster: D - disease causing.
   PolyPhen2: D - probably damaging (≥0.957); P- possibly damaging
   ((0.447 ≤ pp2_hdiv ≤ 0.909); Phylop - the higher is the value, the
   higher the pathogenicity of the variant; *somatic mutations.

3.5. Protein-protein interaction (PPI) network and hub gene analysis

   From 23 proteins, consists of 75 nodes, 23 edges (PPI enrichment value
   0.0568, and average local clustering coefficient of 0.253) a PPI
   network was constructed. Gene Ontology (GO) analysis showed that
   overlapping miR-153 target genes were mainly enriched in collagen type
   IV trimer, basement membrane, extracellular matrix and cytoplasm.
   Regarding molecular function classification, miR-153 target genes were
   enriched in these functions: transforming growth factor beta binding,
   ATPase-coupled intramembrane lipid transporter activity, nucleoside
   monophosphate kinase activity, collagen binding and ATPase activity,
   coupled to movement of substances. The extracellular matrix–receptor
   (ECM-receptor) pathway was the most commonly seen enrichment pathway in
   KEGG (FDR = 0.03) ([80]Supplementary Table S1). STRING analysis of
   protein-protein interaction network of metastatic PC is presented in
   [81]Fig. 7.

Fig. 7.

   [82]Fig. 7
   [83]Open in a new tab

   The miR-153-target regulatory interaction network. Protein-protein
   interaction (PPI) analysis showed that overlapping miR-153 target genes
   were mainly enriched in collagen type IV trimer, basement membrane,
   extracellular matrix and cytoplasm.

4. Discussion

   Patients who have undergone a radical prostatectomy remain at risk of
   biochemical recurrence after surgery, even though they have increased
   survival rates [[84]18]. PC prognostic biomarkers are important to
   facilitate optimization of existing treatment strategies. Recently, it
   has been shown that miRNAs are intimately related to the development of
   several different types of cancers and may be useful to determine
   therapeutic targets for effective treatment strategies in a variety of
   cancers [[85]19,[86]20]. Xie et al. evidenced that miR-520a inhibits
   non-small cell lung cancer (NSCLC) progression through suppression of
   ribonucleoside-diphosphate reductase subunit M2 (RRM2) and Wnt
   signaling pathways [[87]21]. Dong et al. show that miR-369 expression
   is reduced in hepatocellular carcinoma (HCC) tissues [[88]22].

   MiR-516a-3p expression is suppressed in PC tissue, and loss of
   miR-516a-3p expression promotes PC progression through ATP binding
   cassette subfamily C member 5 (ABCC5) targeting [[89]23]. In 2020 Wang
   et al. presented evidence supporting the fact that miR-1231 expression
   is decreased in PC tissues and cell lines and that reduced expression
   of this miRNA had significant association with presence LNM, TNM stage,
   and clinical stage [[90]24]. This research suggests that miRNAs play an
   important role in the process of cancer progression.

   Previous studies have shown that miR-153 is aberrantly expressed in
   several common cancers. Zhang et al. performed miRNAs profiling and
   showed that miR-153 is overexpressed in tissues of advanced colorectal
   cancer (CC). The miRNAs upregulation was further noted in primary CC
   compared, unlike normal colorectal epithelium [[91]25]. Further studies
   showed miR-153 plays a role in promotion of colorectal malignancy
   progression through invasion and chemotherapy resistance enhancement.
   Hua et al. showed miR-153 to be activated in HCC cells with correlation
   between increased expression and poor outcome [[92]26]. In our study we
   compare expression levels of miR-153 in tumor and normal prostate
   tissues in patients with metastatic PC and initial stages of the
   disease and identify specific pathogenic mutations in miR-153 gene
   targets in PC patients. We showed that miR-153 expression rates are
   significantly increased in PC tissue, when compared to normal prostate
   tissue. Moreover, high expression of miR-153 was found significantly
   associated with TNM stage. Our results suggest that it may act as an
   oncogene in PC and may be involved in the development of PC.

   MiR-153 overexpression is able to stimulate the transcriptional
   activity of β-catenin, which leads to cycle progression of cells,
   activation of proliferation and HCC cell colony formation. However,
   decreased expression of miR-153 is observed in some other cancers.
   Interestingly, Zhao et al. show that miR-153 expression is suppressed
   glioma cells compared to normal glial cells [[93]27]. The authors
   showed that miR-153 suppresses cell invasion by regulating the
   expression of the snail family transcriptional repressor 1 (SNAI1),
   which is the target of miR-153. Guo et al. present findings showing
   that miR-153 is significantly overexpressed in patients with
   nasopharyngeal carcinoma (NPC), more so this miRNA affects NPC
   progression through the transforming growth factor-beta 2
   (TGF-β2)/Smad2 signaling pathway [[94]28]. Wang et al. showed that
   miR-153 was overexpressed in BC tissue samples and MDA-MB-231 cells
   [[95]29]. Our study revealed that miR-153 is in fact overexpressed in
   PC tissues. Our results are consistent with Wu et al., who identified
   miR-153 to be overexpressed in PC and showed that miR-153 plays a
   crucial role in increased proliferation of human PC cells and via a
   process of miRNA-mediated suppression of PTEN expression in PC cells
   [[96]8].

   So far, there is very limited data on clinical significance of miR-153
   and its role in PC. Bi et al. found that high miR-153 expression in PC
   tissues closely correlated with burdened clinical manifestations
   [[97]11]. They presented data suggesting that PC patients with high
   levels of miR-153 expression had a lower five-year survival rate, when
   compared with patients with low miR-153 expression levels. Importantly,
   the authors use multivariate Cox regression analysis to show that
   miR-153 rates of expression are independent factors in predicting
   5-year overall outcome in patients with PC. Thus, based on data of the
   present study and the results of previous publications, we can assume
   that miR-153 may serve as an available biomarker for PC prognosis.

   In our study we built upon existing reports of miR-153 significant by
   identifying specific somatic mutations in miR-153 target genes
   ([98]Table 2). One of the mutated genes in metastatic PC patient is
   COL4A1 gene. COL4A1 is involved in epithelial-mesenchymal
   transformation (EMT). In previous studies it was found that depending
   on age of PC patients and Gleason score altered expression of COL4A1
   together with 7 other genes may be an EMT marker among PC patients
   [[99]30]. Mapelli et al. generated a 10-gene predictive classifier
   which showed that COL4A1, a low-luminal marker, supports the
   association of attenuated luminal phenotype with metastatic disease
   [[100]31].

   We also found mutations in the FOXO1 gene in metastatic PC patients.
   The forkhead box O (FOXO) has a common conserved DNA-binding “fork-box”
   domain and in mammals consists of four members: FOXO1, forkhead box
   class O 3a (FOXO3a), forkhead box class O 4 (FOXO4), and forkhead box
   class O 6 (FOXO6). All FOXO factors are involved in a wide range of
   biological processes, including cell cycle arrest, apoptosis, DNA
   repair, glucose metabolism, resistance to oxidative stress and
   longevity [[101]32]. The biological activity of FOXO factors mainly
   depends on posttranslational modification of phosphorylation,
   acetylation or ubiquitination, thereby determining their intracellular
   transport [[102]33]. Dong et al. demonstrated that FOXO1A inhibits
   androgen receptor (AR)-mediated gene regulation and cell proliferation
   in PC [[103]34].

   Another found mutation in PC cells, minichromosome maintenance complex
   component 4 (MCM4), belongs to the minichromosomal maintenance (MCM)
   protein complex which consists of six highly conserved proteins
   (MCM2-7) collectively interacting to promote DNA replication and DNA
   unwinding through replicative helicase activity disinhibition
   [[104]35]. Cancers arising in different anatomic sites are also
   associated with minichromosome maintenance complex component 2 (MCM2),
   MCM4, and minichromosome maintenance complex component 6 (MCM6)
   overexpression, but there is not much information about the role of
   MCM4 in PC [[105][35], [106][36], [107][37]]. It is known that PSA may
   mediate MCM4 to promote the initiation and progression of PC and
   confirmed that PSA knockdown induce the upregulation of MCM4 [[108]38].

   Another important tumor microenvironment component is the
   hypoxia-inducible factor (HIF) pathway. HIF1A was also mutated in
   metastatic PC patient. There are some articles devoted to analysis of
   mutations in HIF1A and their role in PC, although ultimately its role
   in PC remains unknown [[109]39,[110]40].

   KEGG analysis showed that a key signaling pathway in metastatic PC is
   the extracellular matrix (ECM)-receptor interaction signaling. The ECM
   is a non-cellular component of the stroma of tumor. ECM represent a
   complex network of macromolecules which undergo extensive
   reconstruction during tumor progression. Such remodeling of the
   extracellular matrix during cancer progression causes changes in its
   density and composition [[111]41]. Damage to the ECM structure leads to
   reactive growth of tumor cells due to switching of intracellular
   signaling processes and cell cycle changes [[112]42]. Proliferation
   increases, normal tissue architectonics is lost, local migration of
   tumor cells and invasion into surrounding stromal tissue occurs. One of
   the main factors determining the degree of tumor malignancy is the
   process of EMT characterized as loss of epithelial phenotype by
   epithelial cells and acquisition of mesenchymal phenotype associated
   with the ability to migrate through basal membrane. ECM is accompanied
   by loss of cell adhesion molecule E-cadherin, cytokeratins, increased
   N-cadherin, fibronectin, and vimentin [[113]43]. ECM proteins provide
   biochemical signals to induce EMT. In turn, EMT becomes an inducer of
   metastasis, triggering various transcription factors. Thus,
   transformation plays an important role in tumor progression and
   metastasis, involving various transcription factors (TFS) and signaling
   processes [[114]43,[115]44].

5. Conclusions

   Our results show that high miR-153 expression is associated with TNM
   stage increase in PC patients. Several potential PC related genes and
   pathways were identified in the study, which will improve our
   understanding of the molecular mechanisms which support prostate cancer
   progression and development. A key signaling pathway, the ECM-receptor
   interaction signal pathway, was identified as possibly involved in the
   development of PC. Further investigations are needed to perform
   in-depth analysis on gene ontology, browser tracks, and expression
   levels of miR-153 targets in metastatic PC patients.

Compliance with ethical standards

   Ethical approval for this study was obtained from Institute of
   Biochemistry and Genetics Bioethics Committee. The study was carried
   out in accordance to Helsinki Declaration and local guidelines.

Funding

   This work was supported by the Megagrant from the Government of Russian
   Federation № 075-15-2021-595, the Ministry of Education, and Science of
   the Russian Federation № 122041400169-2. In the study, DNA samples from
   the “Collection of Biological Materials of Human Beings” of the IBG
   UFRC RAS were used, supported by the Program of Bioresource Collections
   of the FASO of Russia [Agreement No.007–030164/2]. This work was
   supported by the Bashkir State Medical University Strategic Academic
   Leadership Program (PRIORITY-2030).

Informed consent

   All samples investigated in this study were obtained with written
   informed consents of the participants.

Availability of data and material

   All supporting data and materials are available from the corresponding
   author upon reasonable request.

Author's contribution

   Irina Gilyazova: conceptualization, writing–original draft, and project
   administration. Elizaveta Ivanova: writing–review and editing,
   investigation. Mikhail Sinelnikov: formal analysis, writing–review and
   editing, methodology. Ilgiz Gareev: resources and data curation. Aferin
   Beilerli, Ludmila Mikhaleva, and Yanchao Liang: validation and data
   curation. Valentin Pavlov and Elza Khusnutdinova: validation and
   visualization. Valentin Pavlov and Elza Khusnutdinova: supervision and
   funding acquisition. All authors have read and agreed to the published
   version of the manuscript.

Declaration of competing interest

   The authors declare that they have no known competing financial
   interests or personal relationships that could have appeared to
   influence the work reported in this paper.

Footnotes

   ^Appendix A

   Supplementary data to this article can be found online at
   [116]https://doi.org/10.1016/j.ncrna.2022.10.002.

Appendix A. Supplementary data

   The following is the Supplementary data to this article:
   Multimedia component 1
   [117]mmc1.xlsx^ (12.8KB, xlsx)

References